This is a remote position.
Comerit is looking for an experienced and driven Google AI/ML Data Scientist to join our team and play a key role in the Border Wait Time (BWT) project. In this position, you will prepare and process complex traffic congestion datasets, develop machine learning models to predict traffic volumes, and deploy scalable solutions using Google Vertex AI. Your work will help improve real-time decision-making, enhance border efficiency, and optimize travel experiences for millions of users.
Requirements
Key Responsibilities
- Perform data cleaning, preprocessing, and feature engineering to prepare traffic congestion datasets for predictive modeling.
- Train, validate, and evaluate machine learning models to forecast traffic volumes based on historical trends and key features.
- Deploy machine learning models to Google Vertex AI to enable efficient and scalable prediction services.
- Monitor and validate model performance using test data, ensuring high accuracy and reliability.
- Refine models and methodologies based on performance metrics and stakeholder feedback.
- Collaborate with cross-functional teams, including cloud engineers, data engineers, and integration specialists, to integrate ML solutions into the BWT system.
- Stay updated with the latest advancements in AI/ML and incorporate best practices into project workflows.
Qualifications
Required:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
- 3+ years of experience in machine learning, including data preprocessing, feature engineering, and model development.
- Proficiency in Python and ML libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Hands-on experience with Google Cloud Platform (GCP), particularly Vertex AI, BigQuery, and Cloud Storage.
- Strong understanding of statistical modeling, time series analysis, and predictive analytics.
- Familiarity with version control tools (e.g., Git) and collaborative coding practices.
Preferred:
- Experience working with traffic or congestion datasets.
- Expertise in real-time data integration and analytics.
- Knowledge of containerization tools (e.g., Docker, Kubernetes) for deploying ML models.
- Understanding of data privacy and compliance considerations in cloud-based ML projects.
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